Crash Reporting EDA
Michael Metzler
10/25/2019
Setup
library(tidyverse)
library(lubridate)
library(leaflet)
incidents <- feather::read_feather("incidents.feather")
drivers <- feather::read_feather("drivers.feather")
non_motorists <- feather::read_feather("non_motorists.feather")
single_color <- "#3182bd"
basic_theme <- theme(
plot.title = element_text(size = 24, vjust = .5, hjust = .5),
axis.title = element_text(size = 16, hjust = .5, vjust = .5),
panel.background = element_rect(fill = "white"),
legend.position = "none",
panel.grid.major.y = element_line(color = "grey75", size = .1, linetype = "solid")
)Incidents
This is a quick overview of the structure of the Incidents table.
skimr::skim_to_wide(incidents) %>% DT::datatable()There have been 142 fatal crashes out of 56546 recorded incidents since this data set began in 2015.
incidents %>%
group_by(acrs_report_type) %>%
count() %>%
knitr::kable()| acrs_report_type | n |
|---|---|
| Fatal Crash | 142 |
| Injury Crash | 20237 |
| Property Damage Crash | 36167 |
Location of Incidents
This interactive map shows the locations of the incidents in the data set. The 105 incidents that do not fall within the limits of the county are colored red.
pal <- colorFactor(c("blue", "red"), domain = c(0, 1))
incidents %>%
leaflet() %>%
addTiles() %>%
addCircleMarkers(lng = ~longitude, lat = ~latitude, radius = 3, stroke = TRUE, weight = 2, opacity = 1, color = ~ pal(not_in_county))Time variables
Crashes peak around 5:00 PM, which lines up with the evening rush hour. It seems that the report times are often rounded to the nearest fifteen or five minute round number.
incidents %>%
select(incident_hour, incident_minute) %>%
mutate(incident_time = 60 * incident_hour + incident_minute) %>%
ggplot() +
stat_count(aes(x = incident_time), fill = single_color) +
scale_x_continuous(
name = "Time",
breaks = seq.int(from = 0, to = 24 * 60, by = 120),
labels = c(paste0(seq.int(from = 0, to = 22, by = 2), ":00"), "0:00")
) +
ggtitle("Crashes by Time of Day") +
basic_theme
Crashes are slightly less frequent on the weekends than on weekdays.
incidents %>%
select(incident_weekday) %>%
ggplot() +
stat_count(aes(x = incident_weekday), fill = single_color) +
scale_x_continuous(
name = "Day of Week",
breaks = 1:7,
labels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thurday", "Friday", "Saturday")
) +
ggtitle("Crashes by Day of Week") +
basic_theme
The number of crashes seems to be fairly constant over time.
incidents %>%
select(incident_year) %>%
ggplot() +
stat_count(aes(x = incident_year), fill = single_color) +
ggtitle("Crashes by Year") +
basic_theme
This table contains the number of crashes recorded on each date. 2019 is excluded because the year hasn’t concluded and the counts for dates that already have happened would be inflated. The dates with the most crashes tend to come from the last four months of the year, but holidays including Christmas and New Years are among the days with the fewest crashes.
incidents %>%
filter(incident_year < 2019) %>%
select(incident_month, incident_day) %>%
mutate(incident_date = paste0(incident_month, "/", incident_day)) %>%
count(incident_date) %>%
arrange(desc(n)) %>%
rename(Date = incident_date, `Number of Crashes` = n) %>%
DT::datatable()Weather
Most crashes come under clear weather conditions.
incidents %>%
group_by(weather) %>%
count() %>%
arrange(desc(n)) %>%
knitr::kable()| weather | n |
|---|---|
| CLEAR | 37223 |
| RAINING | 6893 |
| CLOUDY | 5902 |
| N/A | 4764 |
| SNOW | 675 |
| UNKNOWN | 349 |
| FOGGY | 228 |
| WINTRY MIX | 172 |
| OTHER | 138 |
| SLEET | 77 |
| SEVERE WINDS | 64 |
| BLOWING SNOW | 54 |
| BLOWING SAND SOIL DIRT | 7 |
Substance Abuse
Crashes involving alcohol and illegal drugs constitute a small minority of the total incidents.
incidents %>%
mutate(alcohol = driver_substance_abuse_alcohol_present | driver_substance_abuse_alcohol_contributed) %>%
count(alcohol) %>%
knitr::kable()| alcohol | n |
|---|---|
| FALSE | 53354 |
| TRUE | 3192 |
incidents %>%
count(driver_substance_abuse_alcohol_contributed) %>%
knitr::kable()| driver_substance_abuse_alcohol_contributed | n |
|---|---|
| 0 | 55683 |
| 1 | 863 |
incidents %>%
count(driver_substance_abuse_alcohol_present) %>%
knitr::kable()| driver_substance_abuse_alcohol_present | n |
|---|---|
| 0 | 54214 |
| 1 | 2332 |
incidents %>%
mutate(illegal_drug = driver_substance_abuse_illegal_drug_present | driver_substance_abuse_illegal_drug_contributed) %>%
count(illegal_drug) %>%
knitr::kable()| illegal_drug | n |
|---|---|
| FALSE | 56329 |
| TRUE | 217 |
incidents %>%
count(driver_substance_abuse_illegal_drug_contributed) %>%
knitr::kable()| driver_substance_abuse_illegal_drug_contributed | n |
|---|---|
| 0 | 56501 |
| 1 | 45 |
incidents %>%
count(driver_substance_abuse_illegal_drug_present) %>%
knitr::kable()| driver_substance_abuse_illegal_drug_present | n |
|---|---|
| 0 | 56374 |
| 1 | 172 |
Alcohol and Time
Crashes involving alcohol do not follow the general time of day trends of crashes as a whole. The late night and early morning hours see more crashes than the daylight hours.
incidents %>%
filter(driver_substance_abuse_alcohol_present | driver_substance_abuse_alcohol_contributed) %>%
select(incident_hour, incident_minute) %>%
mutate(incident_time = 60 * incident_hour + incident_minute) %>%
ggplot() +
stat_count(aes(x = incident_time), fill = single_color) +
scale_x_continuous(
name = "Time",
breaks = seq.int(from = 0, to = 24 * 60, by = 120),
labels = paste0(seq.int(from = 0, to = 24, by = 2), ":00")
) +
ggtitle("Crashes Involving Alcohol by Time of Day") +
basic_theme
Weekends see more alchol related crashes than weekdays, the opposite of the trend for all crashes.
incidents %>%
filter(driver_substance_abuse_alcohol_present | driver_substance_abuse_alcohol_contributed) %>%
select(incident_weekday) %>%
ggplot() +
stat_count(aes(x = incident_weekday), fill = single_color) +
scale_x_continuous(
name = "Day of Week",
breaks = 1:7,
labels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thurday", "Friday", "Saturday")
) +
ggtitle("Crashes Involving Alcohol by Day of Week") +
basic_theme
Drivers
Overview of the drivers table structure.
skimr::skim_to_wide(drivers) %>% DT::datatable()Driver Injuries
81.31% of drivers did not sustain any reported injuries. 0.92% of Drivers suffered serious or fatal injuries.
ordered_levels <- drivers %>%
count(injury_severity) %>%
arrange(desc(n)) %>%
pull(injury_severity)
drivers %>%
mutate(`Injury Severity` = factor(injury_severity, ordered_levels)) %>%
ggplot() +
stat_count(aes(x = `Injury Severity`), fill = single_color) +
ggtitle("Severity of Driver Injuries") +
basic_theme
drivers %>%
count(injury_severity) %>%
arrange(n) %>%
knitr::kable()| injury_severity | n |
|---|---|
| FATAL INJURY | 70 |
| SUSPECTED SERIOUS INJURY | 861 |
| SUSPECTED MINOR INJURY | 7580 |
| POSSIBLE INJURY | 10330 |
| NO APPARENT INJURY | 81993 |
Vehichle Damage
Most reported accidents involved at least some vehicle damage. Just 3.74% of drivers reported no vehicle damage.
ordered_levels <- drivers %>%
count(vehicle_damage_extent) %>%
arrange(desc(n)) %>%
pull(vehicle_damage_extent)
drivers %>%
mutate(`Vehicle Damage` = factor(vehicle_damage_extent, ordered_levels)) %>%
ggplot() +
stat_count(aes(x = `Vehicle Damage`), fill = single_color) +
ggtitle("Severity of Vehicle Damage") +
basic_theme
drivers %>%
count(vehicle_damage_extent) %>%
arrange(desc(n)) %>%
knitr::kable()| vehicle_damage_extent | n |
|---|---|
| DISABLING | 35708 |
| FUNCTIONAL | 27284 |
| SUPERFICIAL | 26431 |
| DESTROYED | 3951 |
| NO DAMAGE | 3777 |
| UNKNOWN | 3441 |
| N/A | 185 |
| OTHER | 57 |
Speed Limits
drivers %>%
ggplot() +
stat_count(aes(x = speed_limit, fill = ), fill = single_color) +
ggtitle("Speed Limits") +
basic_theme
ordered_injury <- drivers %>%
count(injury_severity) %>%
arrange(desc(n)) %>%
pull(injury_severity)
drivers %>%
mutate(
`Speed Limit` = factor(c("LOW", "MEDIUM", "HIGH")[1 + findInterval(speed_limit, c(31, 46))], levels = c("LOW", "MEDIUM", "HIGH")),
`Injury Severity` = factor(injury_severity, ordered_injury)
) %>%
ggplot() +
stat_count(aes(x = `Injury Severity`, fill = `Speed Limit`)) +
basic_theme +
theme(legend.position = c(.9, .9)) +
ggtitle("Injuries and Speed Limits")
Types of Vehicles
ordered_levels <- drivers %>%
count(vehicle_body_type) %>%
arrange(desc(n)) %>%
pull(vehicle_body_type)
drivers %>%
mutate(`Body Type` = factor(as.character(fct_collapse(vehicle_body_type, OTHER = ordered_levels[-(1:5)])), c(ordered_levels[1:5], "OTHER"))) %>%
ggplot() +
stat_count(aes(x = `Body Type`), fill = single_color) +
ggtitle("Vehicle Body Types") +
basic_theme
Non-Motorists
Overview of Non-Motorists table structure
skimr::skim_to_wide(non_motorists) %>% DT::datatable()Types of Non Motorists
Pedestrians are the most common type of non motorist, followed by bicyclists.
ordered_levels <- non_motorists %>%
count(pedestrian_type) %>%
arrange(desc(n)) %>%
pull(pedestrian_type)
non_motorists %>%
mutate(pedestrian_type = factor(pedestrian_type, ordered_levels)) %>%
ggplot() +
stat_count(aes(x = pedestrian_type), fill = single_color) +
basic_theme +
ggtitle("Types of Non-Motorists")
Locations of Incidents Involving Non-Motorists
pal <- colorFactor(c("blue", "red"), domain = c(0, 1))
non_motorists %>%
leaflet() %>%
addTiles() %>%
addCircleMarkers(lng = ~longitude, lat = ~latitude, radius = 3, stroke = TRUE, weight = 2, opacity = 1, color = ~ pal(not_in_county))Injuries
8.58% of non-motorists did not sustain any reported injuries. 13.09% of non-motorists suffered serious or fatal injuries.
ordered_levels <- non_motorists %>%
count(injury_severity) %>%
arrange(desc(n)) %>%
pull(injury_severity)
non_motorists %>%
mutate(`Injury Severity` = factor(injury_severity, ordered_levels)) %>%
ggplot() +
stat_count(aes(x = `Injury Severity`), fill = single_color) +
ggtitle("Severity of Non Motorist Injuries") +
basic_theme
non_motorists %>%
count(injury_severity) %>%
arrange(n) %>%
knitr::kable()| injury_severity | n |
|---|---|
| FATAL INJURY | 63 |
| NO APPARENT INJURY | 274 |
| SUSPECTED SERIOUS INJURY | 355 |
| POSSIBLE INJURY | 1065 |
| SUSPECTED MINOR INJURY | 1436 |
Bicycle Helmet Usage
ordered_levels <- non_motorists %>%
filter(pedestrian_type == "BICYCLIST") %>%
count(safety_equipment) %>%
arrange(desc(n)) %>%
pull(safety_equipment)
non_motorists %>%
filter(pedestrian_type == "BICYCLIST") %>%
mutate(`Safety Equipment` = factor(safety_equipment, ordered_levels)) %>%
ggplot() +
stat_count(aes(x = `Safety Equipment`), fill = single_color) +
basic_theme +
ggtitle("What Kinds of Safety Equiptment Do Bicyclists Use?")
The more severe injuries tend to have lower rates of helmet usage than minor injuries.
ordered_safety <- non_motorists %>%
filter(pedestrian_type == "BICYCLIST") %>%
count(safety_equipment) %>%
arrange(desc(n)) %>%
pull(safety_equipment)
ordered_injury <- non_motorists %>%
filter(pedestrian_type == "BICYCLIST") %>%
count(injury_severity) %>%
arrange(desc(n)) %>%
pull(injury_severity)
non_motorists %>%
filter(pedestrian_type == "BICYCLIST") %>%
mutate(
`Safety Equipment` = fct_collapse(safety_equipment, OTHER = ordered_safety[-(1:2)]),
`Injury Severity` = factor(injury_severity, ordered_injury)
) %>%
ggplot() +
stat_count(aes(x = `Injury Severity`, fill = `Safety Equipment`)) +
basic_theme +
theme(legend.position = c(.9, .9)) +
ggtitle("Injuries and Helmets")
Consistency Check
fatal_drivers <- drivers %>%
filter(injury_severity_fatal_injury == 1) %>%
pull(report_number)
fatal_nonm <- non_motorists %>%
filter(injury_severity_fatal_injury == 1) %>%
pull(report_number)
fatal_acrs <- incidents %>%
filter(acrs_report_type_fatal_crash == 1) %>%
pull(report_number)
recorded_fatal <- c(fatal_drivers, fatal_nonm)
# vector of fatal reports with no recorded fatality
probs <- fatal_acrs %>% .[!. %in% recorded_fatal]incidents %>%
filter(report_number %in% probs) %>%
DT::datatable(options = list(pageLength = 5))drivers %>%
filter(report_number %in% probs) %>%
DT::datatable(options = list(pageLength = 5))non_motorists %>%
filter(report_number %in% probs) %>%
DT::datatable(options = list(pageLength = 5))drivers %>%
group_by(report_number) %>%
tally() %>%
arrange(desc(n))## # A tibble: 56,167 x 2
## report_number n
## <chr> <int>
## 1 MCP12130045 9
## 2 MCP2667000H 8
## 3 MCP1227000M 7
## 4 MCP15800085 7
## 5 MCP23580027 7
## 6 MCP2513001C 7
## 7 MCP2617006L 7
## 8 MCP3140000Y 7
## 9 MCP9130001S 7
## 10 MCP9422000Z 7
## # ... with 56,157 more rowsdrivers %>%
count(report_number) %>%
count(n) %>%
rename(`Vehicles Involved` = n) %>%
knitr::kable()| Vehicles Involved | nn |
|---|---|
| 1 | 17435 |
| 2 | 33810 |
| 3 | 4089 |
| 4 | 693 |
| 5 | 113 |
| 6 | 17 |
| 7 | 8 |
| 8 | 1 |
| 9 | 1 |
non_motorists %>%
group_by(report_number) %>%
tally() %>%
arrange(desc(n))## # A tibble: 3,034 x 2
## report_number n
## <chr> <int>
## 1 MCP229800LT 4
## 2 MCP2546002K 4
## 3 DD5603004V 3
## 4 DM8445001H 3
## 5 DM8457000R 3
## 6 DM8463000L 3
## 7 EJ7809000W 3
## 8 EJ7833003W 3
## 9 MCP1438002S 3
## 10 MCP20080044 3
## # ... with 3,024 more rows